# 创建项目结构
$ mkdir -p iot-sensor
$ cd iot-sensor
$ echo 'FROM alpine:3.18
RUN apk add --no-cache python3 py3-pip
WORKDIR /app
COPY requirements.txt ./
RUN pip3 install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python3", "sensor.py"]' > Dockerfile
$ echo 'paho-mqtt' > requirements.txt
$ echo 'import paho.mqtt.client as mqtt
import random
import time

# MQTT客户端
client = mqtt.Client()

# 连接回调
def on_connect(client, userdata, flags, rc):
    print("Connected with result code " + str(rc))

# 设置回调
client.on_connect = on_connect

# 连接MQTT broker
client.connect("mqtt", 1883, 60)

# 启动MQTT循环
client.loop_start()

# 模拟传感器数据
while True:
    # 生成随机温度和湿度
    temperature = round(random.uniform(20, 30), 2)
    humidity = round(random.uniform(40, 60), 2)
    
    # 发布数据
    client.publish("sensors/temperature", str(temperature))
    client.publish("sensors/humidity", str(humidity))
    
    print(f"Published temperature: {temperature}, humidity: {humidity}")
    
    # 等待10秒
    time.sleep(10)
' > sensor.py

# 构建镜像
$ docker build -t iot-sensor:1.0 .

# 启动MQTT broker
$ docker run -d --name mqtt -p 1883:1883 eclipse-mosquitto:2.0

# 运行传感器容器
$ docker run -d --name iot-sensor --link mqtt:mqtt iot-sensor:1.0

# 查看传感器数据
$ docker exec -it mqtt mosquitto_sub -h localhost -t sensors/#

sensors/temperature 25.5
sensors/humidity 50.2
sensors/temperature 26.8
sensors/humidity 48.7

风哥提示：使用Docker容器可以快速部署和管理IoT传感器设备，提高设备的可移植性和一致性。

4.2 数据采集与处理实战

案例：使用InfluxDB和Grafana处理IoT数据

# 创建项目结构
$ mkdir -p iot-data-processing
$ cd iot-data-processing
$ cat > docker-compose.yml << 'EOF'
version: '3'
services:
  mqtt:
    image: eclipse-mosquitto:2.0
    ports:
      - "1883:1883"
  influxdb:
    image: influxdb:2.6
    ports:
      - "8086:8086"
    volumes:
      - influxdb_data:/var/lib/influxdb2
  grafana:
    image: grafana/grafana:9.3
    ports:
      - "3000:3000"
    volumes:
      - grafana_data:/var/lib/grafana
  telegraf:
    image: telegraf:1.25
    volumes:
      - ./telegraf.conf:/etc/telegraf/telegraf.conf
    depends_on:
      - mqtt
      - influxdb
volumes:
  influxdb_data:
  grafana_data:
EOF

$ cat > telegraf.conf << 'EOF'
[[inputs.mqtt_consumer]]
  servers = ["mqtt:1883"]
  topics = ["sensors/#"]
  data_format = "value"
  data_type = "float"

[[outputs.influxdb_v2]]
  urls = ["http://influxdb:8086"]
  token = "your-token"
  organization = "my-org"
  bucket = "sensors"
EOF

# 启动服务
$ docker-compose up -d

# 配置InfluxDB
# 1. 访问 http://localhost:8086
# 2. 创建用户、组织和桶
# 3. 生成API token

# 配置Grafana
# 1. 访问 http://localhost:3000
# 2. 登录（默认用户名/密码：admin/admin）
# 3. 添加InfluxDB数据源
# 4. 创建仪表板展示传感器数据

# 启动Portainer
$ docker run -d -p 9000:9000 --name portainer --restart always -v /var/run/docker.sock:/var/run/docker.sock portainer/portainer-ce:latest

# 启动Prometheus和Grafana
$ cat > docker-compose.yml << 'EOF'
version: '3'
services:
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
  node-exporter:
    image: prom/node-exporter:latest
    ports:
      - "9100:9100"
  cadvisor:
    image: gcr.io/cadvisor/cadvisor:latest
    ports:
      - "8080:8080"
    volumes:
      - /:/rootfs:ro
      - /var/run:/var/run:ro
      - /sys:/sys:ro
      - /var/lib/docker/:/var/lib/docker:ro
EOF

$ cat > prometheus.yml << 'EOF'
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'node'
    static_configs:
    - targets: ['node-exporter:9100']
  - job_name: 'cadvisor'
    static_configs:
    - targets: ['cadvisor:8080']
EOF

# 启动服务
$ docker-compose up -d

# 配置Grafana
# 1. 访问 http://localhost:3000
# 2. 登录（默认用户名/密码：admin/admin）
# 3. 添加Prometheus数据源
# 4. 导入仪表板（ID：1860 for Node Exporter，ID：193 for cAdvisor）